High Volume Computing: Identifying and Characterizing Throughput Oriented Workloads in Data Centers

TL;DR

This paper introduces high volume computing (HVC), categorizes throughput-oriented data center workloads, compares HVC with other paradigms, and discusses metrics and benchmarks for designing HVC systems.

Contribution

It systematically identifies and characterizes HVC workloads, compares them with existing paradigms, and initiates the development of metrics and benchmarks for HVC systems.

Findings

Identified three categories of HVC workloads: services, data processing, and real-time applications.

Compared HVC with high throughput, warehouse-scale, and cloud computing paradigms.

Proposed foundational metrics and benchmarks for HVC system evaluation.

Abstract

For the first time, this paper systematically identifies three categories of throughput oriented workloads in data centers: services, data processing applications, and interactive real-time applications, whose targets are to increase the volume of throughput in terms of processed requests or data, or supported maximum number of simultaneous subscribers, respectively, and we coin a new term high volume computing (in short HVC) to describe those workloads and data center computer systems designed for them. We characterize and compare HVC with other computing paradigms, e.g., high throughput computing, warehouse-scale computing, and cloud computing, in terms of levels, workloads, metrics, coupling degree, data scales, and number of jobs or service instances. We also preliminarily report our ongoing work on the metrics and benchmarks for HVC systems, which is the foundation of designing innovative data center computer systems for HVC workloads.